Motion data generation system, motion data generation method, and motion data generation program of robot

ABSTRACT

A motion data generation system of a robot including an upper body, a waist, and a lower body is provided. The motion data generation system includes a subject motion data acquisition unit that acquires upper body motion data captured from motion of the upper body of a subject and captured waist motion data captured from motion of the waist of the subject, a manually generated motion data acquisition unit that acquires lower body motion data and manually generated waist motion data, the lower body motion data and the manually generated waist motion data being generated through manual input by a user, and a robot motion control unit. The robot motion control unit includes a leg state determination unit and a waist motion data generation unit.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Japanese Patent Application No.2021-051969 filed on Mar. 25, 2021, incorporated herein by reference inits entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to a motion data generation system, amotion data generation method, and a motion data generation program of arobot, and particularly a motion data generation system, a motion datageneration method, and a motion data generation program of a humanoidrobot.

2. Description of Related Art

In a motion data generation system disclosed in Japanese UnexaminedPatent Application Publication No. 2012-223864 (JP 2012-223864 A),motion data is acquired from motion of a subject. A humanoid robot canoperate based on this motion data.

SUMMARY

The inventors of the present application have discovered the followingissue.

A robot may be required to be operated at maximum performance of therobot itself depending on the part of the robot. In the motion datagenerated by the motion data generation system described above, motionof the robot is limited by motion of a subject. Therefore, depending onthe part of the robot, it may not be possible to realize motion in whichperformance of the robot itself is fully demonstrated.

The present disclosure has been made in view of such an issue, and anobject of the present disclosure is to provide a motion data generationsystem, a motion data generation method, and a motion data generationprogram of a robot, which generate motion data that enable motion inwhich performance of the robot itself is fully demonstrated.

The motion data generation system of the robot according to the presentdisclosure is a motion data generation system of a robot including anupper body, a waist, and a lower body. The motion data generation systemincludes a subject motion data acquisition unit that acquires upper bodymotion data captured from motion of the upper body of a subject andcaptured waist motion data captured from motion of the waist of thesubject, a manually generated motion data acquisition unit that acquireslower body motion data and manually generated waist motion data, thelower body motion data and the manually generated waist motion databeing generated through manual input by a user, a robot motion controlunit that includes a leg state determination unit and a waist motiondata generation unit. The leg state determination unit determineswhether at least one leg of the robot is in a swing state or both legsof the robot are in a stance state. When the leg state determinationunit determines that at least the one leg of the robot is in the swingstate, the waist motion data generation unit generates the manuallygenerated waist motion data as robot waist motion data. When the legstate determination unit determines that the both legs of the robot arein the stance state, the waist motion data generation unit generates thecaptured waist motion data as the robot waist motion data. The robotmotion control unit controls the upper body, the waist, and the lowerbody of the robot based on the upper body motion data, the robot waistmotion data, and the lower body motion data, respectively.

According to such a configuration, when at least the one leg of therobot is in the swing state, the waist of the robot is operated based onthe manually generated waist motion data. Therefore, the robot does noteasily lose its balance, and it is possible to operate the robotaccording to the performance of the robot itself.

In a transition period for transitioning from a stance phase in whichthe both legs of the robot are in the stance state to a swing phase inwhich at least the one leg of the robot is in the swing state, the waistmotion data generation unit may generate the robot waist motion data bycombining the manually generated waist motion data and the capturedwaist motion data.

According to such a configuration, the robot waist motion data in thetransition period is composite data of the manually generated waistmotion data and the captured waist motion data. Therefore, significantdisplacement of the waist position of the robot is suppressed withrespect to the upper body and the lower body of the robot. That is,unnatural motion of the waist of the robot is suppressed.

In the robot waist motion data generated by the composite data, areference ratio for referring to the manually generated waist motiondata may be gradually increased with respect to the captured waistmotion data from start to end of the transition period.

According to such a configuration, in the transition period, it ispossible to transition to the swing phase while ensuring thecharacteristics of the waist motion based on the captured waist motiondata. Therefore, significant displacement of the waist position of therobot is suppressed with respect to the upper body and the lower body ofthe robot.

The motion data generation method according to the present disclosure isa motion data generation method of a robot to be executed in a motiondata generation system of the robot including an upper body, a waist,and a lower body. The motion data generation method includes a step ofacquiring upper body motion data captured from motion of the upper bodyof a subject and captured waist motion data captured from motion of thewaist of the subject, a step of acquiring lower body motion data andmanually generated waist motion data, the lower body motion data and themanually generated waist motion data being generated through manualinput by a user, a step of determining whether at least one leg of therobot is in a swing state or both legs of the robot are in a stancestate, a step of generating the manually generated waist motion data asrobot waist motion data when determination is made that at least the oneleg of the robot is in the swing state, and generating the capturedwaist motion data as the robot waist motion data when determination ismade that the both legs of the robot are in the stance state, and a stepof controlling the upper body, the waist, and the lower body of therobot based on the upper body motion data, the robot waist motion data,and the lower body motion data, respectively.

According to such a configuration, when at least the one leg of therobot is in the swing state, the waist of the robot is operated based onthe manually generated waist motion data. Therefore, the robot does noteasily lose its balance, and it is possible to operate the robotaccording to the performance of the robot itself.

A motion data generation program according to the present disclosure isa motion data generation program of a robot to be executed by a computeroperating as an arithmetic device in a motion data generation system ofthe robot including an upper body, a waist, and a lower body. The motiondata generation program causes the computer to execute a step ofacquiring upper body motion data captured from motion of the upper bodyof a subject and captured waist motion data captured from motion of thewaist of the subject, a step of acquiring lower body motion data andmanually generated waist motion data, the lower body motion data and themanually generated waist motion data being generated through manualinput by a user, a step of determining whether at least one leg of therobot is in a swing state or both legs of the robot are in a stancestate, a step of generating the manually generated waist motion data asrobot waist motion data when determination is made that at least the oneleg of the robot is in the swing state, and generating the capturedwaist motion data as the robot waist motion data when determination ismade that the both legs of the robot are in the stance state, and a stepof controlling the upper body, the waist, and the lower body of therobot based on the upper body motion data, the robot waist motion data,and the lower body motion data, respectively.

According to such a configuration, when at least the one leg of therobot is in the swing state, the waist of the robot is operated based onthe manually generated waist motion data. Therefore, the robot does noteasily lose its balance, and it is possible to operate the robotaccording to the performance of the robot itself.

The present disclosure can provide a motion data generation system, amotion data generation method, and a motion data generation program of arobot, which generate motion data that enable motion in whichperformance of the robot itself is fully demonstrated.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, advantages, and technical and industrial significance ofexemplary embodiments of the present disclosure will be described belowwith reference to the accompanying drawings, in which like signs denotelike elements, and wherein:

FIG. 1 is a schematic diagram showing a configuration example of a robotto be controlled by a motion data generation system according to a firstembodiment;

FIG. 2 is a block diagram showing a schematic system configuration ofthe motion data generation system according to the first embodiment;

FIG. 3 is a block diagram showing a schematic system configuration of anarithmetic device of the motion data generation system according to thefirst embodiment;

FIG. 4 is a diagram showing positions of markers attached to a subjectand a state in which each marker position has been retargeted for asmall-sized humanoid robot or a long-legged humanoid robot;

FIG. 5 is a diagram showing an example of robot waist motion data in themotion data generation system according to the first embodiment; and

FIG. 6 is a flowchart showing an example of motion of the motion datageneration system according to the first embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Hereinafter, a specific embodiment to which the present disclosure isapplied will be described in detail with reference to the drawings.However, an applicable embodiment of the present disclosure is notlimited to the following embodiment. Further, in order to clarify theexplanation, the following description and drawings have been simplifiedas appropriate.

First Embodiment

A first embodiment will be described with reference to FIGS. 1 to 4.FIG. 1 is a schematic diagram showing a configuration example of a robotto be controlled by a motion data generation system according to thefirst embodiment.

As shown in FIG. 1, a robot 100 includes a lower body 103, a waist 102,and an upper body 101.

The lower body 103 may include at least two legs. The lower body 103according to the first embodiment has a configuration corresponding toeach of upper legs, lower legs, and feet of a person. The lower body 103supports the waist 102 and the upper body 101. The lower body 103 mayinclude two legs, but may include three or more legs.

The waist 102 connects the lower body 103 and the upper body 101. Thewaist 102 causes the posture to be changed, for example. The postureincludes a three-dimensional position and rotation of the joint.

The upper body 101 may have a configuration corresponding to at leastone of a head, a neck, a torso, arms, hands, and fingers of a person.The upper body 101 according to the first embodiment has a configurationcorresponding to each of the head, the torso, the arms, and the hands ofa person.

FIG. 2 is a block diagram showing a schematic system configuration ofthe motion data generation system according to the first embodiment. Amotion data generation system 10 according to the first embodimentincludes a motion capture device 1 and an arithmetic device 2. Themotion capture device 1 acquires motion data from motion of a subject.The arithmetic device 2 generates motion data of a humanoid robot(humanoid robot) such as a bipedal walking robot based on the motiondata acquired by the motion capture device 1. The motion data generationsystem 10 can generate natural motion data of a humanoid robot that iscloser to the motion of the subject.

The motion capture device 1 may be any device that acquires positions ofthe respective joints of the upper body and the waist of the subject.The motion capture device 1 may also acquire angles of the respectivejoints of the upper body and the waist of the subject. A tracker, asensor, a camera image, or the like may be used for the motion capturedevice 1. The motion capture device 1 according to the first embodimentincludes a plurality of markers 11, a tracker 12 for detecting aposition of each marker 11, a plurality of foot contact sensors 13, anda processing unit 14 for processing motion of each marker 11 detected bythe tracker 12 and an output signal from the foot contact sensor 13(floor reaction force information, etc.).

Each marker 11 is attached to a focused part where motion of a subjectH1 is measured (captured) (see FIG. 4). The focused parts include atleast the upper body and the waist of the subject H1, and include, forexample, elbows, shoulders, a head, arms, a neck, hands, and fingers ofthe subject H1. The tracker 12 detects the position of each marker 11 ina predetermined cycle, and the position of each marker 11 detected bythe tracker 12 is input to the processing unit 14. As described above,the motion data of the focused parts of the subject are acquired. Theprocessing unit 14 performs predetermined processing on the detectedposition data of the marker 11 and outputs the processed motion data(motion capture data) to the arithmetic device 2.

The arithmetic device 2 generates natural humanoid robot motion datathat is closer to the motion of the subject based on the motion data ofthe subject acquired by the motion capture device 1. FIG. 3 is a blockdiagram showing a schematic system configuration of the arithmeticdevice 2 of the motion data generation system 10 according to the firstembodiment. The arithmetic device 2 according to the first embodimentincludes a scaling unit 21, a subject motion data acquisition unit 22, amanually generated motion data acquisition unit 23, and a robot motioncontrol unit 24.

The arithmetic device 2 may further include at least one of a datacorrection unit, a first inverse kinematics arithmetic unit, a targetzero moment point (ZMP) calculation unit, a target center of gravitytrajectory calculation unit, and a second inverse kinematics arithmeticunit, as appropriate. The data correction unit performs correctionprocessing for a ground contact state of toes and the like on the motiondata on which the scaling unit 21 has performed retargeting processing.The first inverse kinematics arithmetic unit performs inverse kinematicsarithmetic operations on the whole body of the humanoid robot based onthe motion data on which the retargeting processing and correctionprocessing have been performed, and calculates each joint angle sequenceof the humanoid robot (time series data of each joint angle). Further,the first inverse kinematics arithmetic unit calculates the ZMPtrajectory, the center of gravity trajectory, the angular momentumtrajectory of the center of gravity, and the like of the humanoid robotbefore dynamic stabilization based on the calculated joint anglesequences. The target ZMP calculation unit is a specific example of thetarget ZMP calculation means, and calculates the target ZMP trajectoryfor stabilizing the motion of the humanoid robot based on the ZMPtrajectory calculated by the first inverse kinematics arithmetic unit.The target center of gravity trajectory calculation unit is a specificexample of the target center of gravity trajectory calculation means,and calculates the target center of gravity trajectory based on the ZMPtrajectory calculated by the first inverse kinematics arithmetic unitand the target ZMP trajectory calculated by the target ZMP calculationunit. The second inverse kinematics arithmetic unit is a specificexample of the second inverse kinematics arithmetic means, and based onthe target center of gravity trajectory calculated by the target centerof gravity trajectory calculation unit, performs the inverse kinematicsarithmetic operations on the whole body of the humanoid robot, andcalculates each joint angle sequence of the humanoid robot. Each jointangle sequence of the humanoid robot thus calculated can be used as partof the motion data.

The arithmetic device 2 is provided with the hardware configurationbased on a microcomputer including, for example, a central processingunit (CPU) 2 a for performing arithmetic processing and the like, aread-only memory (ROM) 2 b in which arithmetic programs and the like tobe executed by the CPU 2 a are stored, and a random access memory (RAM)for temporarily storing processing data and the like. Further, the CPU 2a, the ROM 2 b, and the RAM 2 c are connected to each other by a databus 2 d.

The scaling unit 21 is a specific example of the scaling means, andperforms well-known retargeting processing on the motion data from themotion capture device 1 in order to adapt the motion data of the subjectto the humanoid robot that is actually operated. The motion dataacquired by the motion capture device 1 is motion based on the length ofeach part of the subject, and the retargeting processing is performed onthe motion data since information on the focused parts (for example, theposition and the posture of the upper body and waist and the angle ofany joint to be used as the motion data) cannot be adapted to thehumanoid robot as it is.

For example, the scaling unit 21 determines the magnification of eachlink of the humanoid robot from the ratio of each link length of thehumanoid robot to be applied and the length of the corresponding part ofthe subject, and performs the retargeting processing.

For example, as shown in FIG. 4, in a case where the retargetingprocessing is performed on the position of each marker 11 attached tothe subject H1 (the position of the focused part), each marker 11 acorresponding to each marker 11 in the motion data approaches each otherwhen the robot 100 a that is an example of the robot 100 is small.Further, the robot 100 b that is an example of the robot 100 has longlegs. Each marker 11 b corresponding to each marker 11 in the motiondata of the robot 100 b is separated from each other as compared withthat in the motion data of the robot 100 a.

The subject motion data acquisition unit 22 acquires the motion data onwhich the scaling unit 21 has performed the retargeting processing. Theacquired motion data includes upper body motion data captured frommotion of the upper body of the subject H1 and captured waist motiondata captured from motion of the waist of the subject H1.

The manually generated motion data acquisition unit 23 acquires lowerbody motion data and manually generated waist motion data generatedthrough manual input by a user. The lower body motion data and themanually generated waist motion data may be acquired from the ROM 2 b ofthe arithmetic device 2, or may be acquired through manual input by theuser via an interface or the like. This lower body motion data includesa step pattern, foot posture, and posture of the waist 102 thatestablishes these.

The robot motion control unit 24 controls the upper body 101, the waist102, and the lower body 103 of the robot 100. The robot motion controlunit 24 includes a leg state determination unit 25 and a waist motiondata generation unit 26.

Here, there is a swing phase in which at least one leg of the robot 100is in a swing state while the robot 100 is operated. In addition, thereis a stance phase in which both legs of the robot 100 are in a stancestate. In many cases, the swing phase and the stance phase arealternately repeated while the robot 100 is operated.

The leg state determination unit 25 determines whether at least one legof the robot 100 is in a swing state or both legs of the robot 100 arein a stance state. Specifically, the leg state determination unit 25 candetermine whether at least one leg of the robot 100 is in a swing statebased on the lower body motion data acquired by the manually generatedmotion data acquisition unit 23. More specifically, the leg statedetermination unit 25 determines whether at least one leg of the robot100 is in a swing state for each time according to the time in the lowerbody motion data. The leg state determination unit 25 determines whetherat least one leg of the robot 100 is in a swing state, and estimateswhether at least one leg of the robot 100 is in a swing phase or bothlegs of the robot 100 are in a stance phase at the current time.

When the leg state determination unit 25 determines that at least oneleg of the robot 100 is in a swing state, the waist motion datageneration unit 26 selects the manually generated waist motion data, andgenerates the manually generated waist motion data as robot waist motiondata. On the other hand, when the leg state determination unit 25determines that both legs of the robot 100 are in a stance state, thewaist motion data generation unit 26 selects the captured waist motiondata, and generates the captured waist motion data as the robot waistmotion data.

The robot motion control unit 24 operates the waist 102 of the robot 100based on the robot waist motion data. In other words, the robot motioncontrol unit 24 operates the waist 102 based on the manually generatedwaist motion data during the swing phase. In addition, the robot motioncontrol unit 24 operates the waist 102 based on the captured waistmotion data during the stance phase. In a transition period fortransitioning from the stance phase to the swing phase, the robot motioncontrol unit 24 may smoothly transition the motion of the waist 102 fromthe motion based on the captured waist motion data to the motion basedon the manually generated waist motion data. The transition period maybe provided on the stance phase side.

FIG. 5 is a diagram showing an example of the robot waist motion data inthe motion data generation system according to the first embodiment. Asshown in FIG. 5, the waist 102 is operated based on the manuallygenerated waist motion data during the swing phase in principle. On theother hand, the waist 102 is operated based on the captured waist motiondata in the stance phase in principle. Further, in the transitionperiod, the waist 102 is operated so as to smoothly transition from themotion based on the captured waist motion data to the motion based onthe manually generated waist motion data.

The waist motion data generation unit 26 may generate the robot waistmotion data by combining the manually generated waist motion data andthe captured waist motion data. The robot motion control unit 24 mayoperate the waist 102 based on the robot waist motion data generated bythis composite data in the transition period for transitioning from thestance phase to the swing phase. As a result, it is possible to suppressa sudden change in the position of the waist 102 due to the displacementof the waist 102 of the robot 100 during the transition period. That is,unnatural motion of the waist 102 is suppressed.

As a specific example of a method of generating composite data, there isa method of generating composite data in which the ratio of referring tothe captured waist motion data or the manually generated waist motiondata (hereinafter referred to as a reference ratio) is increased orreduced with respect to the position of the waist 102 in the transitionperiod. The time when the transition period starts is set as thetransition period start time t_(s), and the time when the transitionperiod ends is set as the transition period end time t_(e).Specifically, at the transition period start time t_(s), the referenceratio for referring to the captured waist motion data is set high withrespect to the manually generated waist motion data. From the transitionperiod start time t_(s) to the transition period end time t_(e), thereference ratio for referring to the manually generated waist motiondata is gradually increased with respect to the captured waist motiondata. In a specific example of the method of generating the compositedata, in the transition period of the composite data, it may be possibleto transition to the swing phase while characteristics of the motion ofthe waist 102 based on the captured waist motion data are kept to someextent.

In a specific example of the method of generating the composite data,linear interpolation can be used. As a specific example of the linearinterpolation, there is a method of obtaining the posture Pt of thewaist 102 of the robot 100 by using the following formula (1). In otherwords, a relationship between the posture Pt of the waist 102 of therobot 100, the transition period start time t_(s), the transition periodend time t_(e), the posture Pht of the waist 102 based on the manuallygenerated waist motion data, and the posture Pct of the waist 102 basedon the captured waist motion data is shown by the following formula (1).

$\begin{matrix}\left\lbrack {{Formula}1} \right\rbrack &  \\{{Pt} = {{\frac{t - t_{s}}{t_{e} - t_{s}}{Pht}} + {\left( {1 - \frac{t - t_{3}}{t_{e} - t_{s}}} \right)P_{ct}}}} & (1)\end{matrix}$

When the phase is switched from the swing phase to the stance phase, therobot motion control unit 24 obtains the posture Prt of the waist 102using the motion of the waist 102 based on the manually generated waistmotion data as a reference. In such a case, a relationship between theposture Prt of the waist 102 at the time t, the posture Prt_(c) of thewaist 102 at the switching time t_(c) for switching from the swing phaseto the stance phase, a constant A for standardizing the waist posture ofthe subject H1 to a size of the robot 100, the posture Pht of the waist102 based on the captured waist motion data at the time t, and theposture Pht_(c) of the waist 102 based on the captured motion data atthe switching time t_(c) is shown by the following formula (2). That is,the posture Prt of the waist 102 can be obtained by using the followingformula (2).

[Formula 2]

Prt=Prt _(c) +A(Pht−Pht _(c))  (2)

The robot motion control unit 24 generates the motion data by combiningthe upper body motion data acquired by the subject motion dataacquisition unit 22, the lower body motion data acquired by the manuallygenerated motion data acquisition unit 23, the manually generated waistmotion data, the captured waist motion data, and the composite data.

Further, the robot motion control unit 24 may perform stabilizationprocessing on the front-rear and right-left direction components of therobot 100 in the posture Prt of the waist 102 according to the targetZMP trajectory to correct the motion data. As described above, thetarget ZMP trajectory can be obtained when the arithmetic device 2includes the data correction unit, the first inverse kinematicsarithmetic unit, the target ZMP calculation unit, and the like. Therobot motion control unit 24 generates a control signal based on themotion data, and sends the control signal to the upper body 101, thewaist 102, and the lower body 103 of the robot 100. The upper body 101,the waist 102, and the lower body 103 can be operated based on thecontrol signal.

Example of Motion

Next, an example of the motion of the motion data generation systemaccording to the first embodiment will be described with reference toFIG. 6. FIG. 6 is a diagram showing an example of a method of generatinga waist posture in an example of the motion in the motion datageneration system according to the first embodiment. In a flowchartshown in FIG. 6, each of three swim lanes is assigned to the upper body,the waist, and the lower body.

The motion capture device 1 acquires the motion data of the upper bodyand the waist captured from the subject H1 (step ST11). This acquiredmotion data includes the upper body motion data and the captured waistmotion data.

Subsequently, the scaling unit 21 scales the motion data acquired instep ST11 to the body size of the robot 100 (step ST12).

In parallel with steps ST11 and ST12, the manually generated motion dataacquisition unit 23 acquires the lower body motion data and the manuallygenerated waist motion data (step ST13).

Subsequently, the waist motion data generation unit 26 generates thewaist motion data according to the leg state of the robot 100 (stepST14). Specifically, the leg state determination unit 25 determines theleg state of the robot 100. The waist motion data generation unit 26generates the waist motion data based on this determination result.

Subsequently, the motion data of the robot 100 is generated (step ST2).Specifically, the motion data of the robot 100 is generated by combiningthe upper body motion data acquired in step ST11, the lower body motiondata acquired in step ST13, and the waist motion data acquired in stepST14.

Further, the robot motion control unit 24 may perform stabilizationprocessing on the front-rear and right-left direction components of therobot 100 in the posture Prt of the waist 102 according to the targetZMP trajectory to correct the motion data (step ST3). In such a case,similarly to step ST12, the corrected motion data of the robot 100 isgenerated by combining the upper body motion data, the lower body motiondata, which have been described above, and the waist motion datacorrected in step ST3 (step ST4).

As a result, the motion data of the robot 100 can be generated. Therobot motion control unit 24 operates the upper body 101, the waist 102,and the lower body 103 based on the motion data or the corrected motiondata.

An applicable embodiment of the present disclosure is not limited to theabove embodiment, and can be appropriately modified without departingfrom the spirit. Further, the present disclosure may be carried out byappropriately combining the above embodiment and examples thereof. Thepresent disclosure can also be realized, for example, by causing the CPU2 a to execute computer programs for the processing shown in FIGS. 5 and6. The programs described above are stored using various types ofnon-transitory computer readable media and can be supplied to a computer(a computer including an information notification device). Thenon-transitory computer-readable media include various types of tangiblestorage media. Examples of the non-transitory computer-readable mediainclude magnetic recording media (e.g., flexible disks, magnetic tapes,hard disk drives), magneto-optical recording media (e.g.,magneto-optical disks). Further, this example includes a compact discread-only memory (CD-ROM), a compact disc recordable (CD-R), and acompact disc rewritable (CD-R/W). Further, this example includessemiconductor memories (e.g., mask read only memory (ROM), programmableread only memory (PROM), erasable programmable read only memory (EPROM),flash ROM, random access memory (RAM)). The programs may also besupplied to a computer by various types of transitory computer-readablemedia. Examples of the transitory computer-readable media includeelectrical signals, optical signals, and electromagnetic waves. Thetransitory computer-readable media can supply the programs to a computervia a wired communication path such as an electric wire and an opticalfiber, or a wireless communication path.

Further, in the various embodiments described above, as described in theprocessing procedure in the motion data generation system 10, thepresent disclosure may also take a form as a control method of themotion data generation system 10. Further, it can be said that the aboveprograms are programs for causing the motion data generation system 10to execute such a control method.

What is claimed is:
 1. A motion data generation system of a robotincluding an upper body, a waist, and a lower body, the motion datageneration system comprising: a subject motion data acquisition unitthat acquires upper body motion data captured from motion of the upperbody of a subject and captured waist motion data captured from motion ofthe waist of the subject; a manually generated motion data acquisitionunit that acquires lower body motion data and manually generated waistmotion data, the lower body motion data and the manually generated waistmotion data being generated through manual input by a user; and a robotmotion control unit that includes a leg state determination unit and awaist motion data generation unit, wherein: the leg state determinationunit determines whether at least one leg of the robot is in a swingstate or both legs of the robot are in a stance state; when the legstate determination unit determines that at least the one leg of therobot is in the swing state, the waist motion data generation unitgenerates the manually generated waist motion data as robot waist motiondata; when the leg state determination unit determines that the bothlegs of the robot are in the stance state, the waist motion datageneration unit generates the captured waist motion data as the robotwaist motion data; and the robot motion control unit controls the upperbody, the waist, and the lower body of the robot based on the upper bodymotion data, the robot waist motion data, and the lower body motiondata, respectively.
 2. The motion data generation system of the robotaccording to claim 1, wherein in a transition period for transitioningfrom a stance phase in which the both legs of the robot are in thestance state to a swing phase in which at least the one leg of the robotis in the swing state, the waist motion data generation unit generatesthe robot waist motion data by combining the manually generated waistmotion data and the captured waist motion data.
 3. The motion datageneration system of the robot according to claim 2, wherein in therobot waist motion data generated by composite data, a reference ratiofor referring to the manually generated waist motion data is graduallyincreased with respect to the captured waist motion data from start toend of the transition period.
 4. A motion data generation method of arobot to be executed in a motion data generation system of the robotincluding an upper body, a waist, and a lower body, the motion datageneration method comprising: a step of acquiring upper body motion datacaptured from motion of the upper body of a subject and captured waistmotion data captured from motion of the waist of the subject; a step ofacquiring lower body motion data and manually generated waist motiondata, the lower body motion data and the manually generated waist motiondata being generated through manual input by a user; a step ofdetermining whether at least one leg of the robot is in a swing state orboth legs of the robot are in a stance state; a step of generating themanually generated waist motion data as robot waist motion data whendetermination is made that at least the one leg of the robot is in theswing state, and generating the captured waist motion data as the robotwaist motion data when determination is made that the both legs of therobot are in the stance state; and a step of controlling the upper body,the waist, and the lower body of the robot based on the upper bodymotion data, the robot waist motion data, and the lower body motiondata, respectively.
 5. A motion data generation program of a robot to beexecuted by a computer operating as an arithmetic device in a motiondata generation system of the robot including an upper body, a waist,and a lower body, the motion data generation program causing thecomputer to execute: a step of acquiring upper body motion data capturedfrom motion of the upper body of a subject and captured waist motiondata captured from motion of the waist of the subject; a step ofacquiring lower body motion data and manually generated waist motiondata, the lower body motion data and the manually generated waist motiondata being generated through manual input by a user; a step ofdetermining whether at least one leg of the robot is in a swing state orboth legs of the robot are in a stance state; a step of generating themanually generated waist motion data as robot waist motion data whendetermination is made that at least the one leg of the robot is in theswing state, and generating the captured waist motion data as the robotwaist motion data when determination is made that the both legs of therobot are in the stance state; and a step of controlling the upper body,the waist, and the lower body of the robot based on the upper bodymotion data, the robot waist motion data, and the lower body motiondata, respectively.